Medical Diagnosis System for Glaucoma Diseases Detection Based On Retinal Images Using Data Mining Techniques

Authors

  • Pary G Manonmaniam Sundaranar University, Tirunelveli, India
  • Aramudhan M Dept. of Information Technology, Perunthalaivar Kamarajar Institute of Engineering and Technology, Nedungadu, India
  • Thirumoorthy N MCE, Mahindra college of Engineering, Salem

DOI:

https://doi.org/10.26438/ijcse/v6i12.310314

Keywords:

SVM classifier, glaucoma, K-means, PCA, Fundus images

Abstract

The main objective of this research paper is to present an analysis of different types of data mining techniques for the detection of glaucoma. It is one of the serious eye diseases. The Glaucoma affects the optic nerve in retina. In which the retinal ganglion cells are in dead condition and this leads to permanent loss of vision. So the early detection of glaucoma is needed to prevent the patients from diseases. The Manual analysis of retinal images is fairly time-consuming and accuracy depends on the expertise of the professionals. By the proposed Medical diagnosis system mass screening is possible to help the doctor for take proper treatment.

References

[1] Man deep Singh, Introduction to Biomedical Instrumentation, PHI Learning Pvt. Ltd., 10

[2] Rhee, Douglas J. (August 2013). Porter, Robert S.; Kaplan, Justin L., Eds."Glaucoma". The Merck Manual Home Health Handbook. Retrieved December 12, 2013

[3] Normal and Damaged Optic Nerve, Available at:

[4] Aqueous Fluid Pathway, available at: support/intraocular-pressure.asp

[5] Normal Fundus, Available at: definition-of-fundus-by-medical- Dictionary

[6] R. Chra´stek, M. Wolf, K. Donath, H. Niemen, D. Paulus, T. Hothorn, B. Lausen, R. La¨mmer, C.Y. Mardin, G. Michelson,

[7] “Automated segmentation of the optic nerve head for Diagnosis of glaucoma”, 2005, Medical Image Analysis Vol. 9 Issue 4, PP:

[8] R. Bock, J. Meier, G. Michelson, L. G. Ny´ul1 and J. Honegger Classifying Glaucoma with Image-Based Features from Fundus hodographs”, 2010, Pattern Recognition Lecture Notes in Computer Science, Vol. 14 Issue 3,

[9] Conference on Informatics & Systems (INFOS’07), Cairo-Egypt,

[10] A. A. Haleim, A. R. Youssif, A. Z. Ghalwash, and A. A. Sabry A.R. Gonium, “Optic Disc Detection From Normalized Digital Fundus Images By Means Of A Vessels’ Direction Matched Filter”, 2008 IEEE Transactions on Medical Imaging, Vol. 27 Issue 1

[11] A.W. Reza, C. Eswaran, S. Hati” Automatic Tracing Of Optic Disc And Exudates from Color Fundus Images Using Fixed And Variable Thresholds”, 2009, Journal of Medical Systems, Vol. 33, No. 1,

[12]P. N. Schacknow and J. R. Samples, Eds., the Glaucoma Book A Practical, Evidence-Based Approach to Patient Care, Springer.

[13]Shishir Maheshwari, Ram Bilas Pachori and U Rajendra Acharya, “Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted from Fundus Images,” IEEE Journal of Biomedical and Health Informatics, pp. 1-11, March 2016.

[14]Muhammad Salman Haleem, Liangxiu Han, Jano van Hemert and Alan Fleming, Glaucoma Classification using Regional Wavelet Features of the ONH and its Surroundings,” IEEE Journal of Biomedical,.

[15]A. H. James Lowell, “Optic Nerve Head Segmentation,” IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 23,no. 2, pp. 0278-0062, 2004.

[16]J. J. S. L. Mira Park, “Locating the Optic Disc in a Retinal Image,” 2006.

[17] Kittipol Wising, “Automatic Detection of Optic Disc in Digital Retinal Images,” International Journal of Computer Applications, vol. 90, no. 5, pp. 0975 - 8887, 2014.

[18]R. M. Rangayyan, “Detection of the Optic Nerve Head in Fundus Images of the Retina With Gabor Filters and Phase Portrait Analysis,” Journal of Digital Imaging, vol. 23, no. 4, pp. 438 - 453, 2010.

[19]L. K. Rashid Jalal Qureshi, “Combining algorithms for automatic detection of optic disc and macula in fundus

[20] Syed Akhter Husain et al, Automated Detection and Classification of Glaucoma from Eye Fundus Images/ (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (2) , 2015, 1217-1224.

[21] C. Jayasree, K. K. Baseer*Predicting Student Performance to Improve their employability by Applying Data Mining and Machine Learning Techniques. International Journal of Computer Sciences and Engineering Open Access Research Paper Vol.-6, Issue-7, July 2018 E-ISSN: 2347-2693.

[22] Tushar Deshmukh et al. / Indian Journal of Computer Science and Engineering Machine Predicts The Diagnosis A Brief Review Of Medical Diagnosis By Machine Learning Techniques

Downloads

Published

2018-12-31
CITATION
DOI: 10.26438/ijcse/v6i12.310314
Published: 2018-12-31

How to Cite

[1]
G. Pary, M. Aramudhan, and N. Thirumoorthy, “Medical Diagnosis System for Glaucoma Diseases Detection Based On Retinal Images Using Data Mining Techniques”, Int. J. Comp. Sci. Eng., vol. 6, no. 12, pp. 310–314, Dec. 2018.

Issue

Vol. 6 No. 12 (2018): IJCSE December Editor

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.